Eli Tziperman

Coastal upwelling is responsible for sustaining some of the oceans' most productive ecosystems. In order to quantify and predict the resulting surface properties, such as sea surface temperature and nutrient content, it is useful to consider the depth (or range of depths) from which the upwelled water originates. Here, we investigate the effects of wind stress variability on the upwelling source depth in the California upwelling system using regional ocean simulations. We test the response of the mean source depth and its full distribution to three wind stress fields with the same temporal mean but different time dependencies of the variability (constant, monthly, and 6‐hourly). The results of our numerical experiments show that upwelling source depth is deeper when the wind stress varies on shorter timescales. We further show that this result is a direct consequence of the nonlinear response of the upwelling source depth to the variable wind stress on short timescales. Additionally, we find that the scaling relations previously predicted for the source depth as a function of a time‐independent wind stress do not seem to hold when the wind stress is time variable. This work highlights the importance of considering both the time‐mean and variability of the wind stress for understanding and predicting the upwelling source depth and corresponding properties of the upper ocean in coastal upwelling zones.

Despite decades of research, our understanding of the role of climate feedbacks in shaping Pleistocene glacial cycles remains incomplete. Here, we investigate the decay of the Laurentide Ice Sheet (LIS) by calculating its surface mass balance (SMB) during the last deglaciation using the isotope‐enabled transient climate model experiment (iTraCE). To do this, we compare two frameworks for calculating the melt rate, employ a parameterization of the refreezing of snow melt and liquid precipitation, and apply statistical downscaling. We compare the SMB to the ice mass rate of change deduced from the ICE‐6G reconstruction from Last Glacial Maximum (LGM; ∼21 ka) until 12 ka. We find that the SMB derived from iTraCE overestimates mass loss from 20 to 18 ka (corresponding to an approximately 60% excess in cumulative mass loss) and more closely aligns with the ICE‐6G after 18 ka. A key finding is that meltwater forcing plays a critical role in shaping SMB evolution. Its inclusion produces abrupt shifts in SMB and improves agreement with ICE‐6G during Heinrich Stadial 1 and the Younger Dryas, highlighting the importance of ocean circulation and sea ice feedbacks in modulating melt rates. During the Bølling–Allerød, strongly negative SMB is consistent with mass loss in ICE‐6G, but dynamic ice loss is required, perhaps triggered by surface melt. These results demonstrate that SMB derived from transient climate simulations can reproduce key features of deglacial ice sheet evolution when meltwater forcing is appropriately included, and underscore the importance of coupled surface and dynamic processes in shaping ice sheet evolution.

Westerly wind bursts (WWBs) have long been known to have a major impact on the development of El Niño events. In particular, they amplify these events, with stronger events associated with a higher number and stronger WWBs. We consider here a noise-driven recharge oscillator model of ENSO. Commonly, WWBs are represented by a state-dependent Gaussian noise that naturally reproduces the amplification of warm events. However, we show that many properties of WWBs and their effects on sea surface temperature (SST) are better captured by a conditional additive and multiplicative (CAM) noise, which presents a promising alternative to represent WWBs. In addition to recovering the sporadic nature of WWBs, CAM noise leads to an asymmetry between El Niño and La Niña events without the need for deterministic nonlinearities. Furthermore, CAM noise generates SST dynamics with a higher frequency of WWBs accompanying the largest events. This suggests that extreme warm events are better modelled by CAM noise. To cover the full spectrum of warm events, we propose a conditional noise model in which the wind stress is modelled by additive Gaussian noise for sufficiently small SSTs and by additive CAM noise once the SST exceeds a certain threshold. We show that this conditional noise model captures observed bulk statistical properties of ENSO equally well as the commonly used multiplicative Gaussian red noise model, but additionally better reproduces dynamical signatures such as the increased number of WWBs preceding large El Niño events.

Internal modes of climate variability, such as El Niño and the North Atlantic Oscillation, can have a strong influence on distant weather patterns, effects that are referred to as “teleconnections”. The extent to which anthropogenic climate change has and will continue to affect these teleconnections, however, remains uncertain. Here, we employ a covariance fingerprinting approach to demonstrate that shifts in teleconnection patterns affecting monthly temperatures between the periods 1960–1990 and 1990–2020 are attributable to anthropogenic forcing. We further apply multilinear regression to assess the regional contributions and statistical significance of changes in five key climate modes: the El Niño–Southern Oscillation, North Atlantic Oscillation, Southern Annular Mode, Indian Ocean Dipole, and the Pacific Decadal Oscillation. In many regions, observed changes exceed what would be expected from natural variability alone, further implicating an anthropogenic influence.

Evergreen needleleaf forests span a wide climatic range, yet their carbon sequestration is increasingly constrained by climate-driven environmental limits. Building on results presented at EGU 2025, which showed a shift in the dominant seasonal control on productivity from atmospheric moisture in the Boreal Hyytiälä forest (Finland, HYY) to soil moisture in the semi-arid Yatir forest (Israel, YAT), we identify the environmental boundary conditions underlying this contrast.Using PAR-saturated conditions to isolate eco-physiological controls on productivity, we derive key climatic and hydrological thresholds from SHAP-based analyses. In YAT, productivity is strongly constrained by deep soil water availability, with a clear threshold at ~15.8 %vol in the deepest measured soil layer (~45 cm). This threshold reflected a seasonal transition where deep soil moisture shifts from limiting productivity (ineffective water retention and root resistance) to supporting shallow root water uptake and productivity during the wet season. This transition coincides with the seasonal minimum in soil temperature imposing peak root resistance, indicating a compounded control on the onset of productivity in this water-limited ecosystem.In contrast, seasonal productivity in HYY is dominated by precipitation, which both sustains evapotranspiration, closely linked to net ecosystem productivity (R=0.96), and likely reflects a favorable cloud and radiation regime. The high historical ratio of diffuse to direct shortwave radiation in HYY (Sdiff:S ~ 3:4) helps to buffer canopy conductance against high vapor pressure deficit (VPD), consistent with the high sensitivity observed at this site (negative productivity response at VPD > 1 kPa). Such atmospheric constraints are lacking in YAT, where diffuse radiation is limited (Sdiff:S ~ 1:4) and VPD shows an order of magnitude larger range.Despite adaptation to such contrasting environments, both forests exhibit a similar optimal air temperature range for productivity (14–20 °C), which highlights a shared physiological optimum across the divergent environmental limitations. Overall, our results demonstrate that carbon sequestration in these systems is not controlled by universal drivers, but by site-specific boundary conditions, such as deep soil water availability in semi-arid Mediterranean forests and precipitation-linked atmospheric regimes in Boreal forests.

The equatorial jets observed on the Jovian planets—Jupiter, Saturn, Uranus, and Neptune—exhibit extreme zonal flow patterns, manifesting as either strongly prograde (in the gas giants) or strongly retrograde (in the ice giants). Existing theories have often treated gas giants and ice giants separately, primarily focusing on the differences between deep and shallow dynamics. However, gravity measurements from the Juno spacecraft have revealed that Jupiter's convective envelope may share similarities with those of the ice giants, challenging traditional distinctions between these planetary types and highlighting the potential for a unified explanation.We present results from a convection-driven anelastic General Circulation Model that introduces a unifying mechanism to explain the equatorial jets on all four Jovian planets. In these simulations, the convective dynamics and planetary rotation drive the formation of tilted convection columns that extend cylindrically from the deep interior to the outer atmospheric layers. These columns play a crucial role in shaping zonal wind patterns, with the tilting of the convection columns introducing asymmetries in momentum transport that lead to the bifurcation of the flow into either superrotation (prograde jets) or subrotation (retrograde jets) in the equatorial region.Through a detailed analysis of the convection-driven columnar structures, we demonstrate that the equatorial wave properties and the leading-order momentum balance share remarkable similarities across different planetary types. Our findings comprehensively explain the potential for both equatorial superrotation and subrotation under constant physical conditions, thereby elucidating the diverse zonal wind patterns observed on the Jovian planets and providing deeper insight into the mechanisms driving equatorial jet formation. Furthermore, the Juno Microwave Radiometer (MWR) may provide evidence for such tilted convection structures, underscoring the necessity of a thorough understanding of their dynamical contributions.

There is a long history of studies of potential interactions between the El Niño–Southern Oscillation (ENSO) and the quasi-biennial oscillation (QBO). Some suggested that ENSO may modulate QBO phase transitions or amplitude, although identifying a straightforward correlation of the two variability modes has been elusive. Recent studies showed some interesting connections between the surface composites of the two modes, sea surface temperature in particular. However, the observed record is short and noisy, raising the question whether such patterns reflect a robust dynamical coupling or a statistical artifact. In this talk, I will show the observed patterns from ERA5 show and therefore imply. Additionally, I will then discuss how the various high-top CMIP6 models produce (or do not produce) the observed signal. By comparing model output with observations, we assess the extent to which apparent correlations are reproducible by this physical mechanism or can be identified as an artifact.

Westerly wind bursts (WWBs) have long been known to have a major impact on the development of El Niño events. In particular, they amplify these events, with stronger events associated with a higher number of WWBs. We further find indications that WWBs lead to a more monotonically increasing evolution of warming events. We consider here a noise-driven recharge oscillator model of ENSO. Commonly, WWBs are represented by a state-dependent Gaussian noise which naturally reproduces the amplification of warm events. However, we show that many properties of WWBs and their effects on sea surface temperature (SST) are not well captured by such Gaussian noise. Instead, we show that conditional additive and multiplicative (CAM) noise presents a promising alternative. In addition to recovering the sporadic nature of WWBs, CAM noise leads to an asymmetry between El Niño and La Niña events without the need for deterministic nonlinearities. Furthermore, CAM noise generates a more monotonic increase of extreme warming events with a higher frequency of WWBs accompanying the largest events. This suggests that extreme warm events are better modelled by CAM noise. To cover the full spectrum of warm events we propose a conditional noise model in which the wind stress is modelled by additive Gaussian noise for sufficiently small SSTs and by additive CAM noise once the SST exceeds a certain threshold. We show that this conditional noise model captures the observed properties of WWBs reasonably well.

Westerly wind bursts (WWBs) have long been known to have a major impact on the development of El Niño events. In particular, they amplify these events, with stronger events associated with a higher number of WWBs. We further find indications that WWBs lead to a more monotonically increasing evolution of warming events. We consider here a noise-driven recharge oscillator model of ENSO. Commonly, WWBs are represented by a state-dependent Gaussian noise which naturally reproduces the amplification of warm events. However, we show that many properties of WWBs and their effects on sea surface temperature (SST) are not well captured by such Gaussian noise. Instead, we show that conditional additive and multiplicative (CAM) noise presents a promising alternative. In addition to recovering the sporadic nature of WWBs, CAM noise leads to an asymmetry between El Niño and La Niña events without the need for deterministic nonlinearities. Furthermore, CAM noise generates a more monotonic increase of extreme warming events with a higher frequency of WWBs accompanying the largest events. This suggests that extreme warm events are better modelled by CAM noise. To cover the full spectrum of warm events we propose a conditional noise model in which the wind stress is modelled by additive Gaussian noise for sufficiently small SSTs and by additive CAM noise once the SST exceeds a certain threshold. We show that this conditional noise model captures the observed properties of WWBs reasonably well.

Westerly wind bursts (WWBs) have long been known to have a major impact on the development of El Niño events. In particular, they amplify these events, with stronger events associated with a higher number of WWBs. We further find indications that WWBs lead to a more monotonically increasing evolution of warming events. We consider here a noise-driven recharge oscillator model of ENSO. Commonly, WWBs are represented by a state-dependent Gaussian noise which naturally reproduces the amplification of warm events. However, we show that many properties of WWBs and their effects on sea surface temperature (SST) are not well captured by such Gaussian noise. Instead, we show that conditional additive and multiplicative (CAM) noise presents a promising alternative. In addition to recovering the sporadic nature of WWBs, CAM noise leads to an asymmetry between El Niño and La Niña events without the need for deterministic nonlinearities. Furthermore, CAM noise generates a more monotonic increase of extreme warming events with a higher frequency of WWBs accompanying the largest events. This suggests that extreme warm events are better modelled by CAM noise. To cover the full spectrum of warm events we propose a conditional noise model in which the wind stress is modelled by additive Gaussian noise for sufficiently small SSTs and by additive CAM noise once the SST exceeds a certain threshold. We show that this conditional noise model captures the observed properties of WWBs reasonably well.